Comparison of machine learning techniques for predicting energy loads in buildings
Machine learning methods can be used to help design energy-efficient buildings reducing energy loads while maintaining the desired internal temperature. They work by estimating a response from a set of inputs such as building geometry, material properties, project costs, local weather conditions, as...
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| 主要な著者: | , , , |
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| フォーマット: | Online |
| 言語: | eng |
| 出版事項: |
ANTAC - Associação Nacional de Tecnologia do Ambiente Construído
2017
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| オンライン･アクセス: | https://seer.ufrgs.br/ambienteconstruido/article/view/69635 |
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| 要約: | Machine learning methods can be used to help design energy-efficient buildings reducing energy loads while maintaining the desired internal temperature. They work by estimating a response from a set of inputs such as building geometry, material properties, project costs, local weather conditions, as well as environmental impacts. These methods require a training phase which considers a dataset drawn from selected variables in the problem domain. This paper evaluates the performance of four machine learning methods to predict cooling and heating loads of residential buildings. The dataset consists of 768 samples with eight input variables and two output variables derived from building designs. The methods were selected based on exhaustive research with cross validation. Four statistical measures and one synthesis index were used for the performance assessment and comparison. The proposed framework resulted in accurate prediction models with optimized parameters that can potentially avoid modeling and testing various designs, helping to economize in the initial phase of the project. |
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